WO2018072915A1 - Procédé et système pour localiser un véhicule - Google Patents

Procédé et système pour localiser un véhicule Download PDF

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Publication number
WO2018072915A1
WO2018072915A1 PCT/EP2017/071522 EP2017071522W WO2018072915A1 WO 2018072915 A1 WO2018072915 A1 WO 2018072915A1 EP 2017071522 W EP2017071522 W EP 2017071522W WO 2018072915 A1 WO2018072915 A1 WO 2018072915A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
attributes
features
digital map
change
Prior art date
Application number
PCT/EP2017/071522
Other languages
German (de)
English (en)
Inventor
Oliver Pink
Carsten Hasberg
Danny Hiendriana
Christoph Schroeder
Philipp RASP
Original Assignee
Robert Bosch Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch Gmbh filed Critical Robert Bosch Gmbh
Priority to EP17767735.8A priority Critical patent/EP3526546B1/fr
Priority to JP2019520540A priority patent/JP6810257B2/ja
Priority to US16/342,469 priority patent/US11092445B2/en
Priority to CN201780064155.3A priority patent/CN109891192B/zh
Publication of WO2018072915A1 publication Critical patent/WO2018072915A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3804—Creation or updating of map data
    • G01C21/3833—Creation or updating of map data characterised by the source of data
    • G01C21/3837—Data obtained from a single source
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
    • G01C21/30—Map- or contour-matching
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • G05D1/0274—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means using mapping information stored in a memory device

Definitions

  • the invention relates to a method and a system for locating a vehicle.
  • the invention relates to a method and system for locating a vehicle using a digital map.
  • Driver assistance systems are known and widely used for controlling vehicles. Further, there will be a variety of highly automated or fully automated driver assistance functions in the future in which the vehicle is automatically controlled without permanent driver monitoring. Important especially for highly automated and
  • Fully automated driving is the ability of the vehicle or the ability of the corresponding function to be able to make a precise and up-to-date localization of the vehicle at any time. That is, there is a requirement to always have accurate information about where the vehicle is currently located.
  • driver assistance systems and automated driving systems are often dependent on the use of a digital map or an electronic road map, for localization, a comparison of features detected by sensors with those in an available map can be done. Localization relies on the characteristics stored on the card to be current and in line with reality.
  • the document KR2010000386A describes a vehicle navigation system and a method for updating an electronic map, wherein received change data is compared with existing map data and then calculating a percentage of a change rate of a respective map area, wherein the calculated
  • the amount of change may vary depending on the map area. If the percentage calculated in this way exceeds a threshold, the map area automatically updates accordingly
  • the thresholds may differ depending on the area or type of road. Otherwise, the decision on the change will be left to the driver, who will be informed by an ad to help with decision-making.
  • a method for vehicle localization wherein a digital map is used, which describes features, in particular the environment of a vehicle.
  • a digital map is used, which describes features, in particular the environment of a vehicle.
  • the digital card by the digital card one or more predetermined attributes associated with the
  • Characterization of possible occurring changes of the features compared to the presently available digital map are assigned to the characteristics. Further, based on the assigned attributes, respective probabilities for the changes are determined and the vehicle is located taking into account the determined probabilities.
  • a corresponding vehicle location system comprising a digital map and a location unit for locating the vehicle using the digital map. To account for a change in a particular feature over time as compared to the digital map, the digital map has one or more predetermined corresponding attributes with respect to the particular feature. Furthermore, the localization unit is set up to include an associated probability of the change based on the attributes to and when locating the vehicle.
  • the aspects of the invention may improve identification or detection of failed or outdated cards. This is made possible, in particular, by the fact that the digital card, in addition to the conventional information, can record an attribute for the features described by the card, which can be used to determine a current one
  • Change probability for the characteristic is used. This can advantageously be taken into account the fact that especially with an increasing number of features that can be included in more detail with the ongoing development of the localization technology, increasingly be expected that the features are at least partially out of date and instead changes in the meantime available.
  • assigning the one or more predetermined attributes may include using a digital map that has been augmented by the predetermined attributes, wherein the augmentation may be done, for example, by providing an additional digital map layer. Furthermore, the assigning may also include receiving the predetermined attributes by the predetermined attributes.
  • the location of the vehicle may indicate that an assigned attribute of a feature indicates that the probability of a change is increased compared to other features or compared to a previous time. This way lies in this
  • the features fundamentally relate, at least in the first place, to characteristics influenced by humans.
  • the features are subject to both an aging process, which requires their repair in regular cycles, as well as structural changes.
  • the features may preferably relate to objects located in the vicinity of the vehicle, such as lane markers, bridges, buildings, gantries, traffic signs, etc. in the vicinity of the vehicle.
  • the features may also contain more information or information
  • aspects of the invention are not limited to particular features, but may in principle relate to all types of features.
  • a feature may affect not only a single object but also multiple objects and sub-features.
  • a feature in the digital map may relate to an indication of a number of particular objects or features, such as a number of lane markers.
  • the attributes can be represented in particular as an attribute name and an attribute value, wherein the attribute values can be assigned to individual features in a dedicated manner. In other words, each feature may have the corresponding attribute and value.
  • the used attributes can be stored for example in an additional map layer.
  • the features can thus be supplemented by at least one new attribute that relates to a
  • At least one of the attributes has a value specified according to the respective type of a respective feature.
  • different types of features such as signs or road markings, can each be treated similarly or uniformly.
  • the assigned values can be graded in comparison to each other.
  • results of statistical surveys can advantageously be used to determine the species-specific attribute values according to the method.
  • the number of values to be assigned can be limited and limited to a certain number.
  • attributes can be used that are fundamentally neutral to the feature type. These may each relate to information that is directed to a particular feature and may result, for example, from a previous update of the map material or from additional sources of information and investigations.
  • a first one of the attributes is an indication of a cycle duration or rate of change resulting, in particular, from the type of feature
  • a second one of the attributes is a single feature time point of a last change.
  • the attributes are supplemented by the attributes in each case by an indication of the cycle duration or rate of change and an indication of the time of the last change.
  • a model for a temporal evolution of the probability of change of a respective feature is determined.
  • a periodic course can be assumed for the temporal development.
  • This embodiment can be applied particularly advantageously with a cycle duration and a time of the last change as the selected attributes.
  • the course can be assumed to increase overlinear, so that overall results in a sharp, sawtooth-shaped course.
  • the aspects of the invention can thus be used to conveniently determine probable availability or up-to-dateness for each feature under consideration, whereby the reliability of the localization can be increased.
  • the attributes assigned to a respective feature, in particular their values are adapted accordingly.
  • the characteristic values can be changed or assigned for the first time.
  • probability profiles can also be recalculated and / or corrected.
  • system further comprises
  • Attribute detection unit on.
  • existing maps and their features can be extended by the additional procedural attributes, for example by an attribute that describes the rate of change.
  • the attribute detection unit can be connected to a memory for the digital map, in which the detected attributes are stored for assignment, in particular in an additional map layer.
  • the system preferably includes an attribute updating unit for updating the corresponding attributes and their values in response to updating the map data.
  • the update unit can be used with the
  • Memory for the digital card and be adapted to adjust the additional attributes of the invention in a map update and adjust if necessary. This can be changing, deleting, or creating the attribute and the attribute value.
  • features around the vehicle are detected using environmental sensors. Furthermore, a comparison of the detected features with the map data can be made. Furthermore, based on the result of the comparison, the determined probabilities can be consulted. So, if that
  • the sum of the thus prioritized or weighted features can then be processed by a localization filter.
  • This embodiment is particularly advantageous to apply to vehicles that are in an autonomous driving mode.
  • a vehicle such as an autonomous vehicle. There is one
  • Vehicle localization connected and configured so that the method according to the invention can be used to localize the vehicle. Furthermore, according to a further aspect of the invention also a
  • a computer program product provided for use in connection with a vehicle.
  • the computer program product has instructions that implement the method steps described above.
  • the instructions can be loaded into a memory which is connected or can be connected to a control unit of the vehicle. That's it
  • FIG. 1 shows a system for locating a vehicle in an environment, according to an embodiment of the invention
  • Fig. 2 is a diagram of a typical time course for a
  • Fig. 1 shows a system 1 for vehicle localization after a
  • the vehicle 2 is located within the environment 3, the environment 3 here in the
  • a road junction or a road or a part of a highway can be.
  • the environment 3 is characterized by the presence of a plurality of features 4 from which the vehicle 2 can orient itself.
  • the features 4 have for example lane markings 5,
  • the digital map 12 has data for describing the features 4. To illustrate this, are in the drawing occurring in the environment 3
  • features 4 are also shown in the map 12. Furthermore, features 4 in card 12 have attributes 13.
  • the attributes 13 comprise two aspects and have an attribute name 14, or an attribute type, and a value 15, of which in the drawing for the sake of clarity only one is provided with a reference character.
  • FIG. 1 illustrates very diagrammatically the components of the digital map 12 in block diagram form, the digital map 12 may be in data form in real examples.
  • the attributes 13 are provided to extend the conventional map data and their features and attributes by certain attributes 13, which are assigned to the features 4 by the digital map 12.
  • the new attributes 13 have information describing possible changes occurring in the stored features 4 in comparison to the real ones
  • the attributes 13 are designed such that a respective probability for a change or timeliness of a respective feature, in this case, for example, the feature 5 or feature 6, can be derived.
  • a respective probability for a change or timeliness of a respective feature in this case, for example, the feature 5 or feature 6, can be derived.
  • Attribute type 14 an assumed time rate of change of the respective
  • Feature 4 may be a time of a last change of the respective feature 4 known to system 1.
  • an attribute detection unit 16 or an attribute detection and creation unit 16 of the system 1 can be used.
  • the system has an attribute updating unit 17 arranged to update the attributes and their values in response to updating the conventional map data.
  • the system 1 or the vehicle also has an environment sensor system 18. This is intended to detect the environment 3 of the vehicle by means of sensors, such as radar, lidar, ultrasound, etc., and to detect the features 4. By a comparison of features 4 detected by the environmental sensor system 18 with those in the card 12, a precise localization of the vehicle 2 can be carried out. In particular, the position and the orientation relative to the environment can be determined very accurately. Furthermore, by the system 1 and the additional attributes 13, the quality of the card 12
  • control unit 19 of the vehicle 2 is shown.
  • the system 1 according to this embodiment is controlled by the control unit 19 of the vehicle 2.
  • the control unit 19 may control the vehicle 2 in an autonomous drive mode or driver assist mode using the location system 1.
  • the location system 1 provides the position and orientation of the vehicle 2 with respect to the environment 3.
  • Fig. 2 is a diagram of a typical time course of a
  • Probability P of a change of features 4 of a digital map 12 according to an embodiment of the invention is shown.
  • Fig. 2 shows the The abscissa axis shows the time t, and the ordinate axis shows the probability P.
  • the time course of the probability P for the change essentially depends on two parameters. These are here a cycle duration ZK and a time of a last change t 0 .
  • the cycle duration corresponds, for example, to a typical service life or one
  • Maintenance period or generally, a period of time after which usually a change in the feature occurs.
  • the respective quantities or values of t 0 and ZK are entered as values 15 of the attributes 13 for the respective feature 4 of the digital map 12.
  • a change probability of pi is given at the current time ti, it being further to be expected that the change probability P will increase considerably in the near future. Further, at time t 2 is high
  • Time t 3 has not yet been recorded a change, it can be assumed with a certain probability that the map 12 is out of date. This condition can be reported or can be reviewed by System 1, if possible.
  • ti is the new time of the last change. If the change, which as a rule is the case, did not take place exactly at the pre-calculated time ti, the model and the attribute data can be adapted accordingly and a corrected history function with other parameters can be used.
  • Fig. 2 is merely intended to indicate a typical change probability course to be assumed, this is made usable by the appropriate parameters in the added attributes used for the localization of the vehicle.
  • Vehicle localization can be used, which is particularly advantageous especially in autonomous vehicles.
  • FIG. 3 is a block diagram showing aspects of the vehicle localization system and method of the present invention
  • Embodiment of the invention As shown in FIG. 3, both measurements of the environmental sensor system 18 and the data of the digital map 12 enter into a determination of a feature 4. In this way, a network of feature locations that are processed by the location filter 20 is not created. Compared to conventional systems in which only the positions of the various features are fused by the filter and only a general solution for the position and orientation of the vehicle can be output, a weighting and prioritization S10 is made here. The weighting and prioritization S10 intervenes in the case when uncertainties arise in a comparison of the sensor measurement data with the map data.
  • this feature 4 if it has been determined that the feature 4 is already highly changed or no longer up-to-date, this feature 4 may be taken into account correspondingly less or not for the localization. Furthermore, an order of precedence or order of consideration of the features can be generated in an illustrative manner.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Game Theory and Decision Science (AREA)
  • Medical Informatics (AREA)
  • Business, Economics & Management (AREA)
  • Navigation (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

La présente invention concerne un procédé et un système associé de localisation de véhicule par utilisation d'une carte numérique (12). Selon le procédé, la carte numérique (12) est employée pour associer un ou plusieurs attributs (13) prédéterminés qui servent à la caractérisation de modifications effectives éventuelles (A) des caractéristiques (4) par rapport à la carte numérique (12) momentanément en présence, aux caractéristiques (4). En outre, en fonction des attributs (13) associés, des probabilités (P) respectives de changements (A) sont déterminées et le véhicule (2) est localisé en prenant en considération les probabilités (P) déterminées.
PCT/EP2017/071522 2016-10-17 2017-08-28 Procédé et système pour localiser un véhicule WO2018072915A1 (fr)

Priority Applications (4)

Application Number Priority Date Filing Date Title
EP17767735.8A EP3526546B1 (fr) 2016-10-17 2017-08-28 Procédure et système pour la localisation d'une véhicule
JP2019520540A JP6810257B2 (ja) 2016-10-17 2017-08-28 車両を位置特定するための方法およびシステム
US16/342,469 US11092445B2 (en) 2016-10-17 2017-08-28 Method and system for localizing a vehicle
CN201780064155.3A CN109891192B (zh) 2016-10-17 2017-08-28 用于定位车辆的方法和系统

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102016220249.9 2016-10-17
DE102016220249.9A DE102016220249A1 (de) 2016-10-17 2016-10-17 Verfahren und System zur Lokalisierung eines Fahrzeugs

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Publication Number Publication Date
WO2018072915A1 true WO2018072915A1 (fr) 2018-04-26

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US (1) US11092445B2 (fr)
EP (1) EP3526546B1 (fr)
JP (1) JP6810257B2 (fr)
CN (1) CN109891192B (fr)
DE (1) DE102016220249A1 (fr)
WO (1) WO2018072915A1 (fr)

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US11466992B2 (en) * 2020-03-02 2022-10-11 Beijing Baidu Netcom Science And Technology Co., Ltd. Method, apparatus, device and medium for detecting environmental change
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US11604070B2 (en) 2020-03-31 2023-03-14 GM Cruise Holdings LLC. Map maintenance and verification
CN114663992B (zh) * 2022-03-18 2023-06-06 福建工程学院 一种多源数据融合高速公路门架定位方法
CN117421700B (zh) * 2023-12-19 2024-03-19 湖南仕博测试技术有限公司 一种用于自动驾驶中的传感器数据过滤及融合方法及装置

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Also Published As

Publication number Publication date
DE102016220249A1 (de) 2018-04-19
EP3526546A1 (fr) 2019-08-21
US11092445B2 (en) 2021-08-17
JP6810257B2 (ja) 2021-01-06
US20200056893A1 (en) 2020-02-20
EP3526546B1 (fr) 2021-11-10
CN109891192B (zh) 2023-08-01
JP2019533152A (ja) 2019-11-14
CN109891192A (zh) 2019-06-14

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